





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a__      :     Optional[int]         =		logging.get_logger(__name__)

a__      :     List[Any]         =		{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}





class UpperCAmelCase_				( snake_case__ ):
  __lowerCAmelCase   :						Tuple							=   """deta"""
  __lowerCAmelCase   :						Tuple							=   {
      """hidden_size""": """d_model""",
      """num_attention_heads""": """encoder_attention_heads""",
  }


  def __init__(							self ,__snake_case=None ,__snake_case=9_0_0 ,__snake_case=2_0_4_8 ,__snake_case=6 ,__snake_case=2_0_4_8 ,__snake_case=8 ,__snake_case=6 ,__snake_case=1_0_2_4 ,__snake_case=8 ,__snake_case=0.0 ,__snake_case=True ,__snake_case="relu" ,__snake_case=2_5_6 ,__snake_case=0.1 ,__snake_case=0.0 ,__snake_case=0.0 ,__snake_case=0.02 ,__snake_case=1.0 ,__snake_case=True ,__snake_case=False ,__snake_case="sine" ,__snake_case=5 ,__snake_case=4 ,__snake_case=4 ,__snake_case=True ,__snake_case=3_0_0 ,__snake_case=True ,__snake_case=True ,__snake_case=1 ,__snake_case=5 ,__snake_case=2 ,__snake_case=1 ,__snake_case=1 ,__snake_case=5 ,__snake_case=2 ,__snake_case=0.1 ,__snake_case=0.25 ,**__snake_case ,):



       """simple docstring"""





       if backbone_config is None:
            logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
            A_          =       CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4'''] )
       else:
            if isinstance(__snake_case ,__snake_case ):
                 A_          =       backbone_config.pop('''model_type''' )
                 A_          =       CONFIG_MAPPING[backbone_model_type]
                 A_          =       config_class.from_dict(__snake_case )

       A_          =       backbone_config
       A_          =       num_queries
       A_          =       max_position_embeddings
       A_          =       d_model
       A_          =       encoder_ffn_dim
       A_          =       encoder_layers
       A_          =       encoder_attention_heads
       A_          =       decoder_ffn_dim
       A_          =       decoder_layers
       A_          =       decoder_attention_heads
       A_          =       dropout
       A_          =       attention_dropout
       A_          =       activation_dropout
       A_          =       activation_function
       A_          =       init_std
       A_          =       init_xavier_std
       A_          =       encoder_layerdrop
       A_          =       auxiliary_loss
       A_          =       position_embedding_type
       # deformable attributes
       A_          =       num_feature_levels
       A_          =       encoder_n_points
       A_          =       decoder_n_points
       A_          =       two_stage
       A_          =       two_stage_num_proposals
       A_          =       with_box_refine
       A_          =       assign_first_stage
       if two_stage is True and with_box_refine is False:
            raise ValueError('''If two_stage is True, with_box_refine must be True.''' )
       # Hungarian matcher
       A_          =       class_cost
       A_          =       bbox_cost
       A_          =       giou_cost
       # Loss coefficients
       A_          =       mask_loss_coefficient
       A_          =       dice_loss_coefficient
       A_          =       bbox_loss_coefficient
       A_          =       giou_loss_coefficient
       A_          =       eos_coefficient
       A_          =       focal_alpha
       super().__init__(is_encoder_decoder=__snake_case ,**__snake_case )


  @property
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       return self.encoder_attention_heads


  @property
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       return self.d_model




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       copy.deepcopy(self.__dict__ )
       A_          =       self.backbone_config.to_dict()
       A_          =       self.__class__.model_type
       return output







import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							StableDiffusionPipeline.from_pretrained(UpperCamelCase_				,torch_dtype=torch.floataa							)

     # load LoRA weight from .safetensors
     snake_case											=							load_file(UpperCamelCase_							)

     snake_case											=							[]

     # directly update weight in diffusers model
     for key in state_dict:
          # it is suggested to print out the key, it usually will be something like below
          # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

          # as we have set the alpha beforehand, so just skip
          if ".alpha" in key or key in visited:
               continue

          if "text" in key:
               snake_case											=							key.split('''.'''							)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''							)[-1].split('''_'''							)
               snake_case											=							pipeline.text_encoder
          else:
               snake_case											=							key.split('''.'''							)[0].split(LORA_PREFIX_UNET + '''_'''							)[-1].split('''_'''							)
               snake_case											=							pipeline.unet

          # find the target layer
          snake_case											=							layer_infos.pop(0							)
          while len(UpperCamelCase_							) > -1:
               try:
                    snake_case											=							curr_layer.__getattr__(UpperCamelCase_							)
                    if len(UpperCamelCase_							) > 0:
                         snake_case											=							layer_infos.pop(0							)
                    elif len(UpperCamelCase_							) == 0:
                         break
               except Exception:
                    if len(UpperCamelCase_							) > 0:
                         temp_name += "_" + layer_infos.pop(0							)
                    else:
                         snake_case											=							layer_infos.pop(0							)

          snake_case											=							[]
          if "lora_down" in key:
               pair_keys.append(key.replace('''lora_down'''				,'''lora_up'''							)							)
               pair_keys.append(UpperCamelCase_							)
          else:
               pair_keys.append(UpperCamelCase_							)
               pair_keys.append(key.replace('''lora_up'''				,'''lora_down'''							)							)

          # update weight
          if len(state_dict[pair_keys[0]].shape							) == 4:
               snake_case											=							state_dict[pair_keys[0]].squeeze(3							).squeeze(2							).to(torch.floataa							)
               snake_case											=							state_dict[pair_keys[1]].squeeze(3							).squeeze(2							).to(torch.floataa							)
               curr_layer.weight.data += alpha * torch.mm(UpperCamelCase_				,UpperCamelCase_							).unsqueeze(2							).unsqueeze(3							)
          else:
               snake_case											=							state_dict[pair_keys[0]].to(torch.floataa							)
               snake_case											=							state_dict[pair_keys[1]].to(torch.floataa							)
               curr_layer.weight.data += alpha * torch.mm(UpperCamelCase_				,UpperCamelCase_							)

          # update visited list
          for item in pair_keys:
               visited.append(UpperCamelCase_							)

     return pipeline


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE     :					Dict      =       argparse.ArgumentParser()

 parser.add_argument(
     "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
 )
 parser.add_argument(
     "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
 )
 parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
 parser.add_argument(
     "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
 )
 parser.add_argument(
     "--lora_prefix_text_encoder",
     default="lora_te",
     type=str,
     help="The prefix of text encoder weight in safetensors",
 )
 parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
 parser.add_argument(
     "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
 )
 parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")

 _SCREAMING_SNAKE_CASE     :					List[Any]      =       parser.parse_args()

 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       args.base_model_path
 _SCREAMING_SNAKE_CASE     :					Dict      =       args.checkpoint_path
 _SCREAMING_SNAKE_CASE     :					List[str]      =       args.dump_path
 _SCREAMING_SNAKE_CASE     :					Dict      =       args.lora_prefix_unet
 _SCREAMING_SNAKE_CASE     :					List[str]      =       args.lora_prefix_text_encoder
 _SCREAMING_SNAKE_CASE     :					Optional[Any]      =       args.alpha

 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

 _SCREAMING_SNAKE_CASE     :					Tuple      =       pipe.to(args.device)
 pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)




